Transcript Various Change Detection Analysis Techniques

Various Change Detection
Analysis Techniques
Broadly Divided in Two Approaches …..
1.
Post Classification Approach.
2.
Pre Classification Approach.
Post Classification Approach
• Involves the analysis of differences between two independent
categorization products.
Two Methods:
• Spectral or Spectral-spatial (unsupervised) classification.
• Ground truth (supervised) classification.
Advantages:
i.
Data Normalization not required.
Disadvantages:
i.
Consistency.
ii. Error propagation.
Pre Classification Approach
• High Classification accuracy – for areas having significantly different
spectral distributions in the image data.
Various Pre Classification Analysis Approaches:
I.
Composite Analysis.
II. Image Differencing.
III. Principal Components Analysis.
IV. Change Vector Analysis.
V. Spectral Mixture Analysis.
VI. Inner Product Analysis.
VII. Correlation Analysis (Spectral Signature Similarity).
VIII. Image Ratioing.
IX. Albedo Differencing.
Pre Classification Approach…..
Composite Analysis
• Spectral temporal change classification (STCC) by Weismiller et al. (1977).
• Based on single analysis of a multidate data using pattern recognition and spectral
classification.
• Data sets are collected under similar conditions ( e.g. solar zenith angle) but from
different years.
• A composite data set is created then maximum-likelihood classification performed.
Pre Classification Approach…..
Image Differencing
Advantages:
I.
Low cost.
II.
Massive data processing.
Disadvantages:
I.
Optimization of change/no change threshold level.
II.
Subsequent interpretation of image difference product.
• Various Analytical Methods :
I.
Image Subtraction and Thresholding.
II.
Data Transformation, Subtraction and Thresholding.
•
•
•
Band Ratioing.
NDVI.
Tasseled-cap transformation.
Pre Classification Approach…..
Image Differencing ……
Image Subtraction and Thresholding
• Subtraction of two complementary data sets.
• Threshold value is assigned to distinguish spectral differences as area of
LC changes.
• Thresholds are based on Standard Deviation value or may derived from
the histogram of change image.
Pre Classification Approach…..
Image Differencing ……
Data Transformation, Subtraction and Thresholding
• First reduce data dimensionality then data subtraction.
Band Ratioing:
• Calculation of a simple ratio data set.
• Based on band used, a positive or negative subtraction result can provide valuable
insights to changes.
Normalized Difference Vegetation Index (NDVI):
• Most widely used of all vegetation indices.
• Uses only red and near-infrared portion.
• Least affected by topographic features compared to numerous other techniques.
• NDVI = (Near- infrared – Red)/(Near- infrared + Red)
Pre Classification Approach…..
Image Differencing ……
Data Transformation, Subtraction and Thresholding….
Tasseled-Cap Transformation:
• Enhanced data interpretability by emphasizing the structures in the spectral
band .
• For agricultural crop monitoring out results in three vectors,
• Corresponding to brightness.
• Greenness
• Wetness
Pre Classification Approach…..
Principal Components Analysis
• Redundancy reduction technique.
• The first variable or component contains most variance and succeeding
components containing decreasing proportions of data scatter.
• In a multitemporal data,
• PC1 and PC2 tend to represent the unchanged
land cover.
• PC3 and later components contain the changed
land cover information.
Pre Classification Approach…..
Change Vector Analysis
• Defined as the vector difference between the multi-band digital vectors
of the pixel on two different dates.
• The vector describing the direction and magnitude of change from the
first to the second date is a spectral change vector.
Concept:
• Two associated one-band images are computed.
• The first contains the magnitude of the pixel change vector.
• Second contains the direction of the change vectors.
• if the magnitude of the change vector exceeds a specified threshold
criterion, the decision that change has occurred is made .
Pre Classification Approach…..
Change Vector Analysis….
• Once a pixel is identified as having spectral change, the direction image is examined
to determine the type of change.
• The direction of vector contains the information about the type of change.
• For a given image pixel, magnitude is calculated as Euclidean distance (R) between
its location in brightness-greenness space :
Limitation:
• Sensitive to misregistration, mixed pixel and radiometric differences.
Pre Classification Approach…..
Spectral Mixture Analysis
• Based on the premise that multispectral image elements are composed of multiple
spectral signatures or “endmembers” that contribute to the overall image
reflectance.
• A linear mixing model is assumed: the total image reflectance can be calculated by
the proportion of individual endmember area weighted contributions.
• Endmembers can be derived from the image data or from lab spectra.
• Data first converted to reflectance values and that the data be normalized or
corrected for atmospheric effects prior to analysis.
• Major advantage of SMA is to perform spectral unmixing – used to identify subtle LC
changes.
Pre Classification Approach…..
Inner Product Analysis
• Spectral values of a pixel are considered as multispectral vectors.
• The difference between the two multispectral vectors is measured as the cosine of
the angle between them (cos(a)).
Concept:
• If two multispectral vectors coincide each other, their inner product equal to 1.
• If some changes took place between two dates of concerned pixel, the inner
product would be somewhere between -1 to 1.
• A one band image is generate to record the inner product.
• Inner product of two spectral vectors is:
Where x and y are two
spectral vectors.
Pre Classification Approach…..
Correlation Analysis (Spectral Signature Similarity)
• Conceptually similar to inner product method.
• The only difference correlation takes into account the means of the multispectral
vectors.
• This helps in reducing effects due to absolute values of two multispectral vectors.
• Has potential of reducing scene-to-scene radiometric influence induced by
differences in total solar irradiance, sun angles, atmosphere effects and sensors.
• Correlation coefficient for two spectral vectors:
Where x and y are two
spectral vectors.
, , takes the value from -1.0 <
> 1.0
Pre Classification Approach…..
Image Ratioing
• Two co-registered images from different dates with one or more bands in an
image are ratioed, band by band.
Concept:
• Areas without significant spectral change will yield similar ratio values.
• In areas of change, the ratio value would be either higher or lower than the values
in the no-change areas.
Limitations:
• Sensitive to misregistration and existence of mixed pixels.
• To the offsets and gains of the two sensors.
• Ratio image need extensive human interpretation.
Pre Classification Approach…..
Albedo Differencing
• Ratio of the amount of electromagnetic radiance reflected by a body to the
mount of incident upon it.
• Albedo calculated for each pixel is used to create an albedo image (whose DN
values are proportional to the albedo).
• Differencing coregistered image pairs creates quantitative images of the
albedo changes.